Imaging techniques in X-ray, MRI, endoscopy, and ultrasound diagnostics yield a great deal of information that the radiologist or other medical professional has to analyze and evaluate comprehensively in a short time.

CAD systems process digital images or videos for typical appearances and to highlight conspicuous sections, such as possible diseases, in order to offer input to support a decision taken by the professional.

CAD also has potential future applications in digital pathology with the advent of whole-slide imaging and machine learning algorithms.

[1] CAD is an interdisciplinary technology combining elements of artificial intelligence and computer vision with radiological and pathology image processing.

[7] In the late 1950s, with the dawn of modern computers researchers in various fields started exploring the possibility of building computer-aided medical diagnostic (CAD) systems.

[8] These first CAD systems used flow-charts, statistical pattern-matching, probability theory, or knowledge bases to drive their decision-making process.

Thus, by the late 1980s and early 1990s the focus sifted in the use of data mining approaches for the purpose of using more advanced and flexible CAD systems.

In the following years several commercial CAD systems for analyzing mammography, breast MRI, medical imagining of lung, colon, and heart also received FDA approvals.

In CAST systems the FP rate must be extremely low (less than 1 per examination) to allow a meaningful study triage.

Algorithms are generally designed to select a single likely diagnosis, thus providing suboptimal results for patients with multiple, concurrent disorders.

Although much effort has been devoted to creating innovative techniques for these procedures of CAD systems, no single best algorithm has emerged for any individual step.

Moreover, while many positive developments of CAD systems have been proven, studies for validating their algorithms for clinical practice have not been confirmed.

In addition, the lack of training of health professionals on the use of CAD sometimes brings the incorrect interpretation of the system outcomes.

[28] Recent advances in machine learning, deep-learning and artificial intelligence technology have enabled the development of CAD systems that are clinically proven to assist radiologists in addressing the challenges of reading mammographic images by improving cancer detection rates and reducing false positives and unnecessary patient recalls, while significantly decreasing reading times.

In the diagnosis of lung cancer, computed tomography with special three-dimensional CAD systems are established and considered as appropriate second opinions.

To avoid excessive false positives, CAD ignores the normal colon wall, including the haustral folds.

[44] CAD systems with novel image-analysis-based markers as input can aid vascular physicians to decide with higher confidence on best suitable treatment for cardiovascular disease patients.

Reliable early-detection and risk-stratification of carotid atherosclerosis is of outmost importance for predicting strokes in asymptomatic patients.

[46] These combine echogenicity, texture, and motion[47][48][49][50] characteristics to assist clinical decision towards improved prediction, assessment and management of cardiovascular risk.

Computer-aided auscultation is sensitive to external noise and bodily sounds and requires an almost silent environment to function accurately.

Their feature vector of each image is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions chosen according to a specific logic.

[54] In 2010, Wang and Wu presented a forward neural network (FNN) based method to classify a given MR brain image as normal or abnormal.

In 2014, Padma et al. used combined wavelet statistical texture features to segment and classify AD benign and malignant tumor slices.

[65] In 2019, Signaevsky et al. have first reported a trained Fully Convolutional Network (FCN) for detection and quantification of neurofibrillary tangles (NFT) in Alzheimer's disease and an array of other tauopathies.

Intensity variations in areas between periphery and central macular region of the eye have been reported to cause inaccuracy of vessel segmentation.

In contrast, exudates, which appear yellow in normal image, are transformed into bright white spots after green filtering.

[citation needed] SVM is a supervised learning model that belongs to the broader category of pattern recognition technique.

Combination with other pre-processing technique, such as green channel filtering, greatly improves the accuracy of detection of blood vessel abnormalities.

One disadvantage of this technique is that it requires manual selection of seed point, which introduces bias and inconsistency in the algorithm.

[85] The deep learning revolution of the 2010s has already produced AI that are more accurate in many areas of visual diagnosis than radiologists and dermatologists, and this gap is expected to grow.